{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sklearn as skt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'data': array([[ 5.1,  3.5,  1.4,  0.2],\n",
      "       [ 4.9,  3. ,  1.4,  0.2],\n",
      "       [ 4.7,  3.2,  1.3,  0.2],\n",
      "       [ 4.6,  3.1,  1.5,  0.2],\n",
      "       [ 5. ,  3.6,  1.4,  0.2],\n",
      "       [ 5.4,  3.9,  1.7,  0.4],\n",
      "       [ 4.6,  3.4,  1.4,  0.3],\n",
      "       [ 5. ,  3.4,  1.5,  0.2],\n",
      "       [ 4.4,  2.9,  1.4,  0.2],\n",
      "       [ 4.9,  3.1,  1.5,  0.1],\n",
      "       [ 5.4,  3.7,  1.5,  0.2],\n",
      "       [ 4.8,  3.4,  1.6,  0.2],\n",
      "       [ 4.8,  3. ,  1.4,  0.1],\n",
      "       [ 4.3,  3. ,  1.1,  0.1],\n",
      "       [ 5.8,  4. ,  1.2,  0.2],\n",
      "       [ 5.7,  4.4,  1.5,  0.4],\n",
      "       [ 5.4,  3.9,  1.3,  0.4],\n",
      "       [ 5.1,  3.5,  1.4,  0.3],\n",
      "       [ 5.7,  3.8,  1.7,  0.3],\n",
      "       [ 5.1,  3.8,  1.5,  0.3],\n",
      "       [ 5.4,  3.4,  1.7,  0.2],\n",
      "       [ 5.1,  3.7,  1.5,  0.4],\n",
      "       [ 4.6,  3.6,  1. ,  0.2],\n",
      "       [ 5.1,  3.3,  1.7,  0.5],\n",
      "       [ 4.8,  3.4,  1.9,  0.2],\n",
      "       [ 5. ,  3. ,  1.6,  0.2],\n",
      "       [ 5. ,  3.4,  1.6,  0.4],\n",
      "       [ 5.2,  3.5,  1.5,  0.2],\n",
      "       [ 5.2,  3.4,  1.4,  0.2],\n",
      "       [ 4.7,  3.2,  1.6,  0.2],\n",
      "       [ 4.8,  3.1,  1.6,  0.2],\n",
      "       [ 5.4,  3.4,  1.5,  0.4],\n",
      "       [ 5.2,  4.1,  1.5,  0.1],\n",
      "       [ 5.5,  4.2,  1.4,  0.2],\n",
      "       [ 4.9,  3.1,  1.5,  0.1],\n",
      "       [ 5. ,  3.2,  1.2,  0.2],\n",
      "       [ 5.5,  3.5,  1.3,  0.2],\n",
      "       [ 4.9,  3.1,  1.5,  0.1],\n",
      "       [ 4.4,  3. ,  1.3,  0.2],\n",
      "       [ 5.1,  3.4,  1.5,  0.2],\n",
      "       [ 5. ,  3.5,  1.3,  0.3],\n",
      "       [ 4.5,  2.3,  1.3,  0.3],\n",
      "       [ 4.4,  3.2,  1.3,  0.2],\n",
      "       [ 5. ,  3.5,  1.6,  0.6],\n",
      "       [ 5.1,  3.8,  1.9,  0.4],\n",
      "       [ 4.8,  3. ,  1.4,  0.3],\n",
      "       [ 5.1,  3.8,  1.6,  0.2],\n",
      "       [ 4.6,  3.2,  1.4,  0.2],\n",
      "       [ 5.3,  3.7,  1.5,  0.2],\n",
      "       [ 5. ,  3.3,  1.4,  0.2],\n",
      "       [ 7. ,  3.2,  4.7,  1.4],\n",
      "       [ 6.4,  3.2,  4.5,  1.5],\n",
      "       [ 6.9,  3.1,  4.9,  1.5],\n",
      "       [ 5.5,  2.3,  4. ,  1.3],\n",
      "       [ 6.5,  2.8,  4.6,  1.5],\n",
      "       [ 5.7,  2.8,  4.5,  1.3],\n",
      "       [ 6.3,  3.3,  4.7,  1.6],\n",
      "       [ 4.9,  2.4,  3.3,  1. ],\n",
      "       [ 6.6,  2.9,  4.6,  1.3],\n",
      "       [ 5.2,  2.7,  3.9,  1.4],\n",
      "       [ 5. ,  2. ,  3.5,  1. ],\n",
      "       [ 5.9,  3. ,  4.2,  1.5],\n",
      "       [ 6. ,  2.2,  4. ,  1. ],\n",
      "       [ 6.1,  2.9,  4.7,  1.4],\n",
      "       [ 5.6,  2.9,  3.6,  1.3],\n",
      "       [ 6.7,  3.1,  4.4,  1.4],\n",
      "       [ 5.6,  3. ,  4.5,  1.5],\n",
      "       [ 5.8,  2.7,  4.1,  1. ],\n",
      "       [ 6.2,  2.2,  4.5,  1.5],\n",
      "       [ 5.6,  2.5,  3.9,  1.1],\n",
      "       [ 5.9,  3.2,  4.8,  1.8],\n",
      "       [ 6.1,  2.8,  4. ,  1.3],\n",
      "       [ 6.3,  2.5,  4.9,  1.5],\n",
      "       [ 6.1,  2.8,  4.7,  1.2],\n",
      "       [ 6.4,  2.9,  4.3,  1.3],\n",
      "       [ 6.6,  3. ,  4.4,  1.4],\n",
      "       [ 6.8,  2.8,  4.8,  1.4],\n",
      "       [ 6.7,  3. ,  5. ,  1.7],\n",
      "       [ 6. ,  2.9,  4.5,  1.5],\n",
      "       [ 5.7,  2.6,  3.5,  1. ],\n",
      "       [ 5.5,  2.4,  3.8,  1.1],\n",
      "       [ 5.5,  2.4,  3.7,  1. ],\n",
      "       [ 5.8,  2.7,  3.9,  1.2],\n",
      "       [ 6. ,  2.7,  5.1,  1.6],\n",
      "       [ 5.4,  3. ,  4.5,  1.5],\n",
      "       [ 6. ,  3.4,  4.5,  1.6],\n",
      "       [ 6.7,  3.1,  4.7,  1.5],\n",
      "       [ 6.3,  2.3,  4.4,  1.3],\n",
      "       [ 5.6,  3. ,  4.1,  1.3],\n",
      "       [ 5.5,  2.5,  4. ,  1.3],\n",
      "       [ 5.5,  2.6,  4.4,  1.2],\n",
      "       [ 6.1,  3. ,  4.6,  1.4],\n",
      "       [ 5.8,  2.6,  4. ,  1.2],\n",
      "       [ 5. ,  2.3,  3.3,  1. ],\n",
      "       [ 5.6,  2.7,  4.2,  1.3],\n",
      "       [ 5.7,  3. ,  4.2,  1.2],\n",
      "       [ 5.7,  2.9,  4.2,  1.3],\n",
      "       [ 6.2,  2.9,  4.3,  1.3],\n",
      "       [ 5.1,  2.5,  3. ,  1.1],\n",
      "       [ 5.7,  2.8,  4.1,  1.3],\n",
      "       [ 6.3,  3.3,  6. ,  2.5],\n",
      "       [ 5.8,  2.7,  5.1,  1.9],\n",
      "       [ 7.1,  3. ,  5.9,  2.1],\n",
      "       [ 6.3,  2.9,  5.6,  1.8],\n",
      "       [ 6.5,  3. ,  5.8,  2.2],\n",
      "       [ 7.6,  3. ,  6.6,  2.1],\n",
      "       [ 4.9,  2.5,  4.5,  1.7],\n",
      "       [ 7.3,  2.9,  6.3,  1.8],\n",
      "       [ 6.7,  2.5,  5.8,  1.8],\n",
      "       [ 7.2,  3.6,  6.1,  2.5],\n",
      "       [ 6.5,  3.2,  5.1,  2. ],\n",
      "       [ 6.4,  2.7,  5.3,  1.9],\n",
      "       [ 6.8,  3. ,  5.5,  2.1],\n",
      "       [ 5.7,  2.5,  5. ,  2. ],\n",
      "       [ 5.8,  2.8,  5.1,  2.4],\n",
      "       [ 6.4,  3.2,  5.3,  2.3],\n",
      "       [ 6.5,  3. ,  5.5,  1.8],\n",
      "       [ 7.7,  3.8,  6.7,  2.2],\n",
      "       [ 7.7,  2.6,  6.9,  2.3],\n",
      "       [ 6. ,  2.2,  5. ,  1.5],\n",
      "       [ 6.9,  3.2,  5.7,  2.3],\n",
      "       [ 5.6,  2.8,  4.9,  2. ],\n",
      "       [ 7.7,  2.8,  6.7,  2. ],\n",
      "       [ 6.3,  2.7,  4.9,  1.8],\n",
      "       [ 6.7,  3.3,  5.7,  2.1],\n",
      "       [ 7.2,  3.2,  6. ,  1.8],\n",
      "       [ 6.2,  2.8,  4.8,  1.8],\n",
      "       [ 6.1,  3. ,  4.9,  1.8],\n",
      "       [ 6.4,  2.8,  5.6,  2.1],\n",
      "       [ 7.2,  3. ,  5.8,  1.6],\n",
      "       [ 7.4,  2.8,  6.1,  1.9],\n",
      "       [ 7.9,  3.8,  6.4,  2. ],\n",
      "       [ 6.4,  2.8,  5.6,  2.2],\n",
      "       [ 6.3,  2.8,  5.1,  1.5],\n",
      "       [ 6.1,  2.6,  5.6,  1.4],\n",
      "       [ 7.7,  3. ,  6.1,  2.3],\n",
      "       [ 6.3,  3.4,  5.6,  2.4],\n",
      "       [ 6.4,  3.1,  5.5,  1.8],\n",
      "       [ 6. ,  3. ,  4.8,  1.8],\n",
      "       [ 6.9,  3.1,  5.4,  2.1],\n",
      "       [ 6.7,  3.1,  5.6,  2.4],\n",
      "       [ 6.9,  3.1,  5.1,  2.3],\n",
      "       [ 5.8,  2.7,  5.1,  1.9],\n",
      "       [ 6.8,  3.2,  5.9,  2.3],\n",
      "       [ 6.7,  3.3,  5.7,  2.5],\n",
      "       [ 6.7,  3. ,  5.2,  2.3],\n",
      "       [ 6.3,  2.5,  5. ,  1.9],\n",
      "       [ 6.5,  3. ,  5.2,  2. ],\n",
      "       [ 6.2,  3.4,  5.4,  2.3],\n",
      "       [ 5.9,  3. ,  5.1,  1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
      "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
      "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'],\n",
      "      dtype='<U10'), 'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}\n"
     ]
    }
   ],
   "source": [
    "print(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=2, random_state=0).fit(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.00566038,  3.36037736,  1.56226415,  0.28867925],\n",
       "       [ 6.30103093,  2.88659794,  4.95876289,  1.69587629]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L5qqeOjz5367EA59YgSu6/Hh7IHRBrjRngk7cOiqCVru1+x2MuC1yox+toI8WRqfTeHLv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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6c7134908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(dataset.data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FZMEiNsKb597E5/dxuOMwI8ERVrtW89StT9HS2ELjisZUl5jVFNwikrBTF0/ha/Ph8/vo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\ngltEJMMouEVEMsz/B5fOlaqtSCsmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6c6e66128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot((kmeans.cluster_centers_).data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "aglomerative = AgglomerativeClustering(n_clusters = 2).fit(dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aglomerative.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.data\n",
    "y = dataset.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Métricas Homogenity e Completness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \t0.751\n"
     ]
    }
   ],
   "source": [
    "print(\" \\t{0:.3}\".format(metrics.completeness_score(kmeans.predict(X),y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \t0.579\n"
     ]
    }
   ],
   "source": [
    "print(\" \\t{0:.3}\".format(metrics.completeness_score(aglomerative.labels_,y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.cluster import homogeneity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \t0.765\n"
     ]
    }
   ],
   "source": [
    "print(\" \\t{0:.3}\".format(homogeneity_score(kmeans.predict(X),y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \t1.0\n"
     ]
    }
   ],
   "source": [
    "print(\" \\t{0:.3}\".format(homogeneity_score(aglomerative.labels_, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "nC = np.arange(1,10); vetor = []    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "for n in nC:\n",
    "    kmeans.n_clusters = n\n",
    "    kmeans.fit(X)\n",
    "    \n",
    "    vetor.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff6c65dc668>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.xlabel('Numero de clusters')\n",
    "plt.ylabel('inertia')\n",
    "plt.plot(nC,vetor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dcZciInJYRhsQu8xsKuEe0WZ2DqDlvg+iqSFcD7FONxASkfFptAFxM9EtQeeZ2QvAfcCX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\n/NUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff6c66f6eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=3, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=0, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logo o melhor número de clusters é 3\n",
    "kmeans.n_clusters = 3\n",
    "kmeans.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "homogenity = homogeneity_score(y,kmeans.predict(X))\n",
    "completeness = metrics.completeness_score(y,kmeans.predict(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Homogeneity: \t0.751\n"
     ]
    }
   ],
   "source": [
    "print(\"Homogeneity: \\t{0:.3}\".format(homogenity))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completness: \t0.765\n"
     ]
    }
   ],
   "source": [
    "print(\"Completness: \\t{0:.3}\".format(completeness))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
